Shuo Qi

Post doc @williamandmary | PhD @SMUEconDept | Microeconometrics and Networks | Studying Many Unobservables.

[Shuo Qi @June_Qi]

FAULKNER, “All of us failed to match our dream of perfection. So I rate us on the basis of our splendid failure to do the impossible. ” Paris Review - William Faulkner, The Art of Fiction No. 12 https://t.co/HFYnUe9Iog

[Econometrics Papers @eBlogs]

Post-selection inference for network structure

Eric Auerbach, Jonathan Auerbach, Sidonia McKenzie
https://t.co/0hLhU0M4nc [𝚎𝚌𝚘𝚗.𝙴𝙼 𝚜𝚝𝚊𝚝.𝙼𝙴]

[Econometrics Papers @eBlogs]

Causal Inference Using Factor Models

Jushan Bai, Peng Wang
https://t.co/ATEMxu1i9w [𝚎𝚌𝚘𝚗.𝙴𝙼]

[Econometrics Papers @eBlogs]

Decision Theory for the Archetype Discovery Problem

José Luis Montiel Olea, Amilcar Velez, Zhuoheng Xu, Haomin Yu, Shunqi Zhang
https://t.co/MVZD15IbPC [𝚎𝚌𝚘𝚗.𝙴𝙼 𝚜𝚝𝚊𝚝.𝙰𝙿 𝚜𝚝𝚊𝚝.𝙼𝙴]

[Econometrics Papers @eBlogs]

Partial Identification from LLM Prompts

Xiaohong Chen, Elie Tamer
https://t.co/8TZJnAHwoz [𝚎𝚌𝚘𝚗.𝙴𝙼]

[Econometrics Papers @eBlogs]

Random Set Quantile Estimation of Partially Identified Discrete Response Models

Shakeeb Khan, Tatiana Komarova, Denis Nekipelov
https://t.co/0b8fst7WIu [𝚎𝚌𝚘𝚗.𝙴𝙼]

[Econometrics Papers @eBlogs]

Sharp Bounds and Inference in Sample Selection Models with Treatment Endogeneity

Yingying Dong, Phillip Heiler
https://t.co/F0feWxat3W [𝚎𝚌𝚘𝚗.𝙴𝙼]

[Econometrics Papers @eBlogs]

Panel Data Estimation of Individual Demand in Markets with Many Consumers

Sarah Moon, Whitney K. Newey
https://t.co/N7CCajTRdW [𝚎𝚌𝚘𝚗.𝙴𝙼]

[Econometrics Papers @eBlogs]

Higher-Order Neyman Orthogonality in Moment-Condition Models

Stéphane Bonhomme, Koen Jochmans, Whitney K. Newey, Martin Weidner
https://t.co/3yhRu9o64d [𝚎𝚌𝚘𝚗.𝙴𝙼 𝚖𝚊𝚝𝚑.𝚂𝚃]

[The Review of Economic Studies @RevEconStudies]

New paper by Kalouptsidi, Kitamura, Lima, and Souza-Rodrigues:

https://t.co/FHf8vzHePk

#REStud
#EconX
#EconTwitter

[The Review of Economic Studies @RevEconStudies]

Uniform asymptotic theory for local projections with unknown/infinite lag order. LPs can achieve semiparametric efficiency and support robust IRF inference.

New paper by Xu:

https://t.co/dgGo192pTc

#REStud
#EconX
#EconTwitter

[Econometrics Papers @eBlogs]

Misspecification-Averse Estimation

Isaiah Andrews, Ricky Li, Yucheng Shang
https://t.co/mqLMRbgsCb [𝚎𝚌𝚘𝚗.𝙴𝙼]

[The Review of Economic Studies @RevEconStudies]

New paper by Blandhol, Bonney, Mogstad, and Torgovitsky:

https://t.co/BrwFSZTYpx

#REStud
#EconX
#EconTwitter

[Econometrics Papers @eBlogs]

Econometric Inference with Machine-Learned Proxies: Partial Identification via Data Combination

Lixiong Li
https://t.co/9v7ixVnM2O [𝚎𝚌𝚘𝚗.𝙴𝙼]

[The Review of Economic Studies @RevEconStudies]

"We prove a CLT for strategic network formation with homophilous agents. This enables inference on statistics derived from a single large network. We discuss practical procedures."

New Paper by Leung & Moon:

https://t.co/UOVsc5yXZw

#REStud
#EconX
#EconTwitter

[Econometrics Papers @eBlogs]

Optimally-Transported Generalized Method of Moments. https://t.co/q6Y8OSbEz8

[Peter Hull @instrumenthull]

Paul G-P (@paulgp), Michal Kolesár, and I have a new guide to leniency designs (aka judge/examiner IVs), prepared for the JEP

The bottom line? Keep calm and UJIVE on!

[Yiqing Xu @xuyiqing]

Glad this paper with @guido_imbens is out in the JEP.

The LaLonde paper has had a big impact on my academic journey and continues to teach us about the challenges and possibilities of conducting credible inference using nonexperimental data. https://t.co/kfRlFXxULm

Many thanks to Tim for the tireless editing and suggestions. @TimothyTTaylor

[Econometrics Papers @eBlogs]

Unbiased Regression-Adjusted Estimation of Average Treatment Effects in Randomized Controlled Trials. https://t.co/sGIDhcEMZe

[Econometrics Papers @eBlogs]

Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data. https://t.co/oXFbI30MxS

[Jesús Fernández-Villaverde @JesusFerna7026]

Let me explain why I believe modern economics is such a powerful tool for understanding the world. I’ll do this by discussing a great paper by Simone Cerreia-Vioglio, @UncertainLars, Fabio Maccheroni, and Massimo Marinacci, “Making Decisions Under Model Misspecification,” published in the Review of Economic Studies a few months ago.

Imagine I want to drive from UC San Diego to UCLA, but I’ve never driven that route before. I need to build a “model of the world” to guide me, which we usually call a map. Maps are simplified representations of reality. They can’t include every detail if they’re to be useful. Borges, in his short story On Exactitude in Science, makes this point beautifully. (In practice, I don’t draw the map myself—I use an app—but someone still had to make it.)

Because maps simplify, I can’t fully rely on them. Maybe last night’s storm knocked down a tree and closed a street, or there’s construction and the ramp off the highway in LA is shut down.

This uncertainty matters. Suppose I’m driving to UCLA for an important talk at 11 a.m. If the ramp is closed, I might need 15 extra minutes. When should I set my alarm to arrive on time, while still getting enough sleep to give a good talk?

The problem is that I can’t assign precise probabilities to all these contingencies. How likely is the fallen tree? Or new roadwork? Even the best traffic apps can’t capture every disruption, and some might happen after I’ve already left.

In economic terms, my “model of the world” (the map) is misspecified—and no matter how hard I try, I can’t fully fix that.

But sitting down and crying about misspecification doesn’t answer my basic question: when do I set the alarm? Too early, and I’m exhausted. Too late, and I’m late.

Simone and his co-authors offer a way to think about this. They start from the idea that we often hold several structured models of an economic phenomenon, grounded in theory. For example, a central bank might use a standard New Keynesian model and a search-and-matching model of money.

Yet, aware that each model is misspecified by design, the bank adds a protective belt of unstructured models—statistical constructs that help it gauge the consequences of misspecification.

The beauty of the paper is that it provides an axiomatic foundation for this protective belt (and even generalizes it to include a Bayesian approach). It shows that if a decision-maker’s preferences meet certain conditions —reflecting both rational and behavioral features— then those preferences can be represented by an augmented utility function that formally accounts for misspecification.

Crucially, we don’t assume that augmented utility function; we derive it. We start with general, plausible properties of preferences and prove that they imply such a representation.

That’s real progress. Instead of writing endless critiques of expected utility or rational expectations (as many have done for decades, with little to show), we now have a formal way to reason about misspecification—precise definitions, clear boundaries of validity, and awareness of what we still don’t know.

Take, for instance, a brilliant Penn graduate student on the market, Alfonso Maselli

https://t.co/rl2gu95V7t

His job-market paper pushes this frontier further. He studies cases where a decision-maker not only faces model misspecification but is also unsure which model best fits the data and can’t assign probabilities to them—what we call model ambiguity. In my example, the central bank is unsure whether the New Keynesian or the search-and-matching model fits better, and it worries that both might be incorrect.

If you read Simone et al. or Alfonso’s paper, you’ll see how misguided—and, frankly, cartoonish—many of the recent criticisms of economics on X have been.

First: the idea that economists don’t understand math or have “physics envy.” The math in these papers is subtle and advanced—utterly different from what physicists do (neither better nor worse, just distinct). An engineer transitioning into economics would find these tools unfamiliar.

Second: claims of ideological bias are unfounded. I have no idea about the political views of the authors, and I’d be surprised if anyone could infer them from the analysis—beyond vague guesses about typical academics.

Third: This has almost nothing to do with what one learns as an undergraduate, or even in first-year graduate school. If your knowledge of economics stops at an intro textbook, it’s best not to pontificate on the field’s frontiers.

Fourth: Is this science? Debating that word’s boundaries is pointless; every definition of “science” breaks down somewhere.

The Germans solved this long ago with the idea of Wissenschaft—the systematic pursuit of knowledge, whether of nature, society, or the humanities. By that measure, modern mainstream economics is clearly a Wissenschaft: a disciplined, cumulative, and highly useful effort to understand how the world works. Simone and his co-authors have demonstrated that beyond any reasonable doubt.